Senior Staff Machine Learning Engineer, Personalization & Recommendations
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Job Location:
San Francisco, CA - USA
Monthly Salary:
$ 242240 - 344000
Posted on:
15 hours ago
Vacancies:
1 Vacancy
Job Summary
About Quizlet:
At Quizlet our mission is to help every learner achieve their outcomes in the most effective and delightful way. Our $1B learning platform serves tens of millions of students every month including two-thirds of U.S. high schoolers and half of U.S. college students powering over 2 billion learning interactions monthly.
We blend cognitive science with machine learning to personalize and enhance the learning experience for students professionals and lifelong learners alike. Were energized by the potential to power more learners through multiple approaches and various tools.
Lets Build the Future of Learning
Join us to design and deliver AI-powered learning tools that scale across the world and unlock human potential.
About the Team:
The Personalization & Recommendations ML Engineering team builds the core intelligence behind how Quizlet matches learners with content activities and experiences that best fit their goals. We power recommendation and search systems across multiple surfaces from home feed and search results to adaptive study modes.
Our teams objective is to make Quizlet feel uniquely tailored for every learner by combining cutting-edge machine learning scalable infrastructure and insights from learning science.
Youll collaborate closely with Product Managers Data Scientists Platform Engineers and fellow ML engineers to deliver personalized learning pathways that drive engagement satisfaction and measurable learning outcomes.
About the Role:
As a senior technical leader on the Personalization & Recommendations team youll not only architect cutting-edge personalization systems but also guide the strategic direction of Quizlets AI-driven learner experience mentoring peers and influencing decisions across the this role youll architect and implement large-scale retrieval ranking and recommendation systems that directly shape the learner experience. Youll bring modern RecSys expertise (from deep learningbased retrieval and embeddings to multi-task ranking and reinforcement learning) and help evolve Quizlets personalization stack.
Youll help define and deliver systems that learn from billions of interactions while respecting learner privacy fairness and integrity.
Were happy to share that this is an onsite position in our San Francisco office. To help foster team collaboration we require that employees be in the office a minimum of three days per week: Monday Wednesday and Thursday and as needed by your manager or the company. We believe that this working environment facilitates increased work efficiency team partnership and supports growth as an employee and organization.
In this role you will:
- Work closely with other senior leaders to define and drive the long-term technical vision for personalization and recommendations across multiple Quizlet surfaces ensuring alignment between modeling strategy platform capabilities and product roadmaps
- Communicate complex modeling trade-offs and recommendations to diverse audiences (from senior leadership to cross-functional partners) influencing decisions through clear reasoning data and empathy
- Architect and build large-scale personalization models across candidate retrieval ranking and post-ranking layers leveraging user embeddings contextual signals and content features to power adaptive learning experiences
- Develop scalable retrieval and serving systems using modern architectures such as Two-Tower deep ranking and ANN-based vector search for real-time personalization at global scale
- Lead model training evaluation and deployment pipelines for retrieval and ranking systems ensuring training-serving consistency reliability and robust monitoring
- Partner closely with Product and Data Science to translate learning objectives (e.g. engagement retention and mastery) into measurable modeling goals and experimentation frameworks
- Advance evaluation methodologies by refining offline metrics (e.g. NDCG CTR calibration) and online A/B testing to rigorously measure learner impact and model performance
- Collaborate with platform and infrastructure teams to optimize distributed training inference latency and cost-efficient serving in production environments
- Stay at the forefront of personalization and RecSys research bringing relevant advances from top conferences (KDD WSDM SIGIR RecSys NeurIPS) into applied production systems
- Mentor and coach engineers and applied scientists fostering technical excellence reproducibility and responsible AI practices across the organization
- Champion a culture of collaboration inclusivity and experimentation helping elevate Quizlets AI craft and ensuring personalization systems serve learners equitably and effectively
What you bring to the table:
- 12 years of experience in applied machine learning or ML-heavy engineering with deep expertise in personalization ranking or recommendation systems
- Proven ability to shape technical direction across multiple teams or disciplines balancing long-term architectural vision with near-term product and business priorities
- Exceptional communication and storytelling skills able to distill complex technical concepts into clear narratives for executives product partners and non-technical audiences
- Demonstrated leadership through influence guiding teams through ambiguity aligning stakeholders around measurable goals and ensuring accountability for impact
- Experience mentoring senior engineers and applied scientists leading technical working groups and driving cross-team innovation and standardization
- Track record of measurable impact improving key online metrics such as CTR retention and engagement through recommender ranking or search systems in production
- Deep technical understanding of modern retrieval and ranking architectures (e.g. Two-Tower deep cross networks GNNs MMoE Transformers) and multi-stage RecSys pipelines.
- Strong hands-on skills in Python and PyTorch with expertise in data and feature engineering distributed training and inference on GPUs and familiarity with modern MLOps practices including model registries feature stores monitoring and drift detection
- Experience with large-scale embedding models and vector search systems (FAISS ScaNN or similar) including training serving and optimization at scale
- Expertise in experimentation and evaluation connecting offline metrics (AUC NDCG calibration) with online A/B results to drive confident data-informed decisions
- Commitment to collaboration and inclusion fostering a culture that values diverse perspectives constructive debate and shared ownership of results
Bonus points if you have:
- Publications or open-source contributions in RecSys search or ranking
- Familiarity with reinforcement learning for recommendations or contextual bandits
- Experience with hybrid RecSys systems blending collaborative filtering content understanding and LLM-based reasoning
- Prior work in consumer or EdTech applications with personalization at scale
Compensation Benefits & Perks:
- Quizlet is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees. Salary transparency helps to mitigate unfair hiring practices when it comes to discrimination and pay gaps. Total compensation for this role is market competitive including a starting base salary of $242240 - $$344000 depending on location and experience as well as company stock options
- Collaborate with your manager and team to create a healthy work-life balance
- 20 vacation days that we expect you to take!
- Competitive health dental and vision insurance (100% employee and 75% dependent PPO Dental VSP Choice)
- Employer-sponsored 401k plan with company match
- Access to LinkedIn Learning and other resources to support professional growth
- Paid Family Leave FSA HSA Commuter benefits and Wellness benefits
- 40 hours of annual paid time off to participate in volunteer programs of choice
Why Join Quizlet
Massive reach: 60M users 1B interactions per week
Cutting-edge tech: Generative AI adaptive learning cognitive science
Strong momentum: Top-tier investors sustainable business real traction
Mission-first: Work that makes a difference in peoples lives
Inclusive culture: Committed to equity diversity and belonging
We strive to make everyone feel comfortable and welcome!
We work to create a holistic interview process where both Quizlet and candidates have an opportunity to view what it would be like to work together in exploring a mutually beneficial partnership.
We provide a transparent setting that gives a comprehensive view of who we are!
In Closing:
At Quizlet were excited about passionate people joining our teameven if you dont check every box on the requirements list. We value unique perspectives and believe everyone has something meaningful to contribute. Our culture is all about taking initiative learning through challenges and striving for high-quality work while staying curious and open to new ideas. We believe in honest respectful communication thoughtful collaboration and creating a supportive space where everyone can grow and succeed together.
Quizlets success as an online learning community depends on a strong commitment to diversity equity and inclusion.
As an equal opportunity employer and a tech company committed to societal change we welcome applicants from all backgrounds. Women people of color members of the LGBTQ community individuals with disabilities and veterans are strongly encouraged to apply. Come join us!
To All Recruiters and Placement Agencies:
At this time Quizlet does not accept unsolicited agency resumes and/or profiles.
Please do not forward unsolicited agency resumes to our website or to any Quizlet employee. Quizlet will not pay fees to any third-party agency or firm nor will it be responsible for any agency fees associated with unsolicited resumes. All unsolicited resumes received will be considered the property of Quizlet.
#LI-FT
We may use artificial intelligence (AI) tools to support parts of the hiring process such as reviewing applications analyzing resumes or assessing responses. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed please contact us.
Required Experience:
Staff IC
Key Skills
- Computer Science
- Docker
- Kubernetes
- Python
- VMware
- C/C++
- Go
- System Architecture
- gRPC
- OS Kernels
- Perl
- Distributed Systems
About Company
Quizlet makes simple learning tools that let you study anything. Start learning today with flashcards, games and learning tools — all for free.
